{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fist class with jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A very simple operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  you can change to command model with key `ESC`\n",
    "`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## multiple line is also supported :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "  print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## markdown is also support images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://blog.jupyter.org/content/images/2015/02/jupyter-sq-text.png\"\n",
    "style=\"width:100px;height:100px;float:left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## what's more LaTex is also supported"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\int_0^{+\\infty} x^2 dx$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`$$\\int_0^{+\\infty} x^2 dx$$`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## with notebook , you can export as serveral format\n",
    "- HTML\n",
    "- markdown\n",
    "- pdf\n",
    "- ReST\n",
    "- raw python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrated with Matplotlib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use Matplotlib,you should first `pip installl matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6d288b8f98>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ArwXT44GZXmU+0MnM0hq9chGJGO+tyeOWWdn071YV8kkK+YhyXH30ZpYBjAAWAKnuvgOq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\nj787zxrXhFMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d289056a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(20)\n",
    "y = x**2\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
